Y-Rank: A Multi-Feature-Based Keyphrase Extraction Method for Short Text

Abstract

Keyphrase extraction is a critical task in text information retrieval, which traditionally employs both supervised and unsupervised approaches. Supervised methods generally rely on large corpora, which introduce the problems of availability, while unsupervised methods are independent of out-sources but also lead to defects like imperfect statistical features or low accuracy. Particularly in short-text scenarios, limited text features often result in low-quality candidate ranking. To address this issue, this paper proposes Y-Rank, a lightweight unsupervised keyphrase extraction method that extracts the average information content of candidate sentences as the key statistical features from a single document, and follows a graph construction approach based on similarity to obtain the semantic features of keyphrase with high-quality and ranking accuracy. Finally, the top-ranked keyphrases are acquired by the fusion of these features. The experimental results on five datasets illustrate that Y-Rank outperforms the other nine unsupervised methods, achieves enhancements on six accuracy metrics, including Precision, Recall, F-Measure, MRR, MAP, and Bpref, and performs the highest improvement in short text scenarios.